Interest in using image processing to aid in quality control and grading for a variety of agricultural products has recently become a subject of considerable research. In particular, efforts have been made to use computer-assisted imaging techniques to facilitate recognition of defective agricultural products. Computer imaging systems generally include a color video camera connected to a frame grabber. The frame grabber digitizes the image provided by the camera and relays the image information to a computer. Analysis of the digital image information may then be performed using a variety of techniques. In particular, the potential has been shown to discriminate between crop seeds and certain common contaminants based on image parameters such as area, perimeter, aspect ratio, shape factor, and the like. Other applications have involved classification of diploid and tetraploid ryegrass seeds, and orientation determination of vegetables using grey-level intensity gradients and syntactic pattern recognition techniques.
By way of example, a computer vision system for determining soybean quality based on size and shape parameters has been developed (see Misra, et al., Computer Vision for Soybeans, presented at the 1989 International Summer Meeting of American Society of Agricultural Engineers and Canadian Society of Agricultural Engineering, Paper No. 89-3001). Images of a soybean are first captured using a charge-coupled device (CCD) camera and digitized by a frame grabber. The image processing sequence is initiated by determining an outline of the soybean under analysis by searching for contrasts between the portions of the image corresponding to the background and to the soybean itself. A routine is then used to fit an ellipse to the outline, since acceptably healthy soybeans were found to be generally elliptical in shape. While capable of successfully discriminating between soybeans having varying degrees of quality, it is believed that the efficiency of the machine vision system described above could be improved by modification of particular aspects of the disclosed image processing sequence.
Concurrent with the development of the image processing techniques described above, efforts have been made to develop acoustical methods of analysis based on the transmittance, absorption or reflection of sound waves by agricultural products. These techniques are based on the realization that even minor changes in the structure or health of a product will result in variation of its acoustic properties. Such variations can be quantitatively evaluated by analyzing the frequency components of the sound wave. Frequency data is generally processed using analytic procedures such as the Fast Fourier Transform (FFT), which can be performed to identify the ways in which selected frequencies are absorbed, transmitted or reflected by the product being investigated. These frequency response characteristics can be correlated with various physical properties of the product related to quality.
In the particular case of the analysis of soybeans, at least two types of acoustical methods have been investigated (see, e.g., Misra, et al., Acoustic Properties of Soybeans, Transaction of the American Society of Agricultural Engineers, 33(2):671-677). In a first, or "acoustic transmission" technique, a soybean kernel is placed between input and receiving transducers where the former introduces an acoustic impulse to the kernel and the latter records the wave transmitted through the kernel. Both waves, the input and the transmitted, can be digitally recorded and analyzed by a Fast Fourier Transform. The two spectra can then be compared, usually by dividing the transmitted wave by the input wave to identify frequencies that are preferentially absorbed by the kernel so as to provide an indication of kernel quality. Specifically, quality may be determined by analyzing the differences in the absorption spectra of a "good" or reference soybean and the soybean under scrutiny. Unfortunately, the acoustic transmission spectra of an ideal soybean has been found to be difficult to describe mathematically. Accordingly, correlation between the transmission spectra and size or mass of the soybean has not been possible, thus precluding effective quality determination. Moreover, the placement of each soybean between the transducers has been found to be a relatively slow process.
A second, or "impact-force" method of acoustical characterization of soybeans involves dropping soybeans through a guide tube coupled to a piezoelectric force transducer. An impact signal generated by the transducer is routed to a digitizer and then to a computer. A computer program then operates to derive the frequency spectra of the impact signal by using an FFT algorithm. As in the acoustic transmission technique, correlation of the frequency spectra of the impact signal with a set of quality parameters requires the spectra to be mathematically described. Such a description could be effectuated through, for example, polynomial approximations, sine functions, or simple Bessel functions. Although the impact-force method has been shown to allow for faster determination of soybean quality, the frequency-domain procedure outlined above is relatively computationally intensive. That is, the procedure requires an initial FFT conversion of the impact signal into the frequency domain and a subsequent parameterization of the spectral characteristics so obtained. It is thus believed that a time-domain method for analyzing the transducer signal produced by an impact-force apparatus would allow for a more rapid determination of the quality of an agricultural product being investigated.
While image processing and acoustical techniques have each been of separate assistance in determining the quality of agricultural products, a system incorporating both of these methodologies would allow for increased flexibility with respect to the criterion used for quality determination. For example, such an integrated system would allow a user to specify that a set of product characteristics derived from both the acoustical and video reals constitute the basis for acceptable quality.